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source("./tianfengRwrappers.R")
# plan("multiprocess",workers = 8)

读取结果

human_coronary <- readRDS("human_coronary.rds")
CA_dataset1 <- readRDS("CA_dataset1.rds")
CA_dataset2 <- readRDS("CA_dataset2.rds") #已经经过分组处理了

修改分群

基质细胞分类

ECs亚群分析 整合

整合算法可能出现负值,运行SCENIC时舍弃了这些异常值

# 提取内皮细胞亚群
ECs_list <- list(subset(CA_dataset1, idents = "Endothelial"), subset(human_coronary, idents = "Endothelial"))

ECs_list <- lapply(X = ECs_list, FUN = function(x) {
  x <- NormalizeData(x)
  x <- FindVariableFeatures(x, selection.method = "vst", nfeatures = 2000)
})
# 需要分析的差异基因
int_features <- SelectIntegrationFeatures(object.list = ECs_list)
# 选择合并的anchor特征
int_anchors <- FindIntegrationAnchors(object.list = ECs_list, anchor.features = int_features)

# 根据anchor合并
ECs_combined <- IntegrateData(anchorset = int_anchors)

DefaultAssay(ECs_combined) <- "integrated"
rm("ECs_list", "int_features", "int_anchors")
multi_featureplot(c("TNFRSF11B","ACTA2","CNN1","LUM"),human_coronary)

multi_featureplot(c("TNFRSF11B","ACTA2","CNN1","LUM"),CA_dataset1)

multi_featureplot(c("TNFRSF11B","ACTA2","CNN1","LUM"),CA_dataset2)

genes <- c("LGALS3","CD68","KLF4","CLDN5","VWF","ACTA2")
multi_featureplot(genes,CA_dataset2)

Dotplot(genes,CA_dataset2) # 2>5

multi_featureplot(genes,human_coronary)

Dotplot(genes,human_coronary)

差异基因

annoation

cl4_marker <- FindMarkers(ds2,ident.1 = 4,logfc.threshold = 0.5, min.diff.pct = 0.3)

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